Abstract

This paper considers an effective method for nonlinear acoustic echo cancellation (NL-AEC). More specifically, we model the nonlinear echo path by a latent state vector capturing the coefficients of a memoryless processor and a linear finite impulse response filter. To estimate the posterior probability distribution of the state vector, an elitist particle filter based on evolutionary strategies (EPFES) has been proposed, which evaluates realizations of the latent state vector based on long-term fitness measures. This method includes a manually-tuned recursive calculation of the probabilities that the observation has been produced by the state-vector realizations. For avoiding this manual tuning, we introduce a new approach denoted as Elitist Resampling Particle Filtering (ERPF) which can also be shown to combine the advantages of the Sequential Importance Sampling Particle Filter (SIS-PF) and the Sequential Importance Sampling/Resampling Particle Filter (SIR-PF). This new approach allows universal use and leads to superior system identification performance compared to both the original EPFES as well as the SIR-PF, as verified for a simulated scenario and a real smartphone recording.